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Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 2: 385 - 395

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.02.012

Resource Utilization and Carbon Trading

Preference and Willingness to Pay for Waste Mobile Phone Recycling among College Students in Beijing: A Discrete Choice Experiment Study

  • CHEN Chang , 1 ,
  • YAN Maolin 2 ,
  • GE Weiwei 1 ,
  • SHI Wenhua 1 ,
  • ZHANG Xiang 2 ,
  • WU Chengliang 1 ,
  • ZHANG Yang , 1, *
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  • 1. Department of Economics and Management, Beijing Forestry University, Beijing 100083, China
  • 2. State Academy of Forestry and Grassland Administration, Beijing 102600, China
*ZHANG Yang, E-mail:

CHEN Chang, E-mail:

Received date: 2022-09-21

  Accepted date: 2023-04-30

  Online published: 2024-03-14

Supported by

The National Natural Science Foundation of China(71573019)

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Abstract

Based on the Mixed Logit model, the willingness-to-pay (WTP) for waste mobile phone recycling among college students in Beijing was investigated using a discrete choice experiment method. The research results three aspects of respondents’ choices and WTP. (1) Their choices are positively affected by information security, recycling price, recycling method and payment method, and negatively affected by payment amount. (2) Respondents have the highest WTP for information security (30.04 yuan), followed by the recycling price (5.94 yuan) and payment method (4.41 yuan), and the lowest WTP for recycling models (2.87 yuan). (3) Personal socio-economic characteristics such as gender, annual household income, and the number of mobile phones held by respondents have significant impacts on their recycling WTP. The deeper the respondents' awareness of the environmental protection effect of waste mobile phone recycling, the more enthusiasm they have for the recycling behavior, the higher their participation in recycling, and the higher their WTP for recycling.

Key words： choice experiment method; college students; mixed Logit model; waste mobile phone recycling; willingness to pay

Cite this article

CHEN Chang , YAN Maolin , GE Weiwei , SHI Wenhua , ZHANG Xiang , WU Chengliang , ZHANG Yang . Preference and Willingness to Pay for Waste Mobile Phone Recycling among College Students in Beijing: A Discrete Choice Experiment Study[J]. Journal of Resources and Ecology, 2024 , 15(2) : 385 -395 . DOI: 10.5814/j.issn.1674-764x.2024.02.012

1 Introduction

With the continuous growth in demand and the rapid development of technology, mobile phones are being updated and replaced more quickly, and China has become the country with the largest number of disposable mobile phones (Li et al., 2020). In 2019, China’s annual theoretical scrap of waste mobile phones had reached nearly 300 million (Fu, 2021), and the stock of waste mobile phones was as high as 1100 million, but the overall recycling rate was less than 2% (Wang et al., 2018). Some mobile phones that cannot be effectively recycled cause serious harm to the natural environment and human health due to incineration or disposal in landfills (Quintero-Almanza et al., 2019; Soetrisno and Delgado-Saborit, 2020). Idle mobile phones can also lead to the waste of a large amount of metal resources, such as copper, iron and even precious metal resources such as gold, silver and nickel, and consequently the over-exploitation of primary resources (He et al., 2019; Ma and Wang, 2021). Therefore, in order to improve the current status of waste mobile phone recycling, designing and implementing a scientific, efficient and environmentally friendly recycling system for waste mobile phones has become an urgent task (Zhou, 2018; Zhai et al., 2019; Chen, 2021). As the source of the mobile phone recycling system, consumers’ willingness-to-pay (WTP) will directly affect the effectiveness of waste mobile phone recycling (Xu, 2011; Islam et al., 2020). College students, as the most active, knowledgeable, fashion-conscious and willing to accept new things members of society, are the largest potential consumer group of electronic products and the main force of electronic product recycling. Research shows that by 2025, the number of mobile phones owned by college students in China will exceed 72 million. With full recycling, it will create an economic value of 1030 million yuan (Wang et al., 2021a).
Based on these considerations, this study used a discrete choice experiment, taking college students as the research object, to calculate the marginal value of each attribute of waste mobile phone recycling, determine the willingness of college students to pay for the different attributes and analyze the impact of relevant factors on their improved preference for the waste mobile phone recycling system. The analysis in this study can provide a technical reference and data support for improving China’s electronic product recycling system and formulating relevant government recycling policies at the same time.

2 Literature review

Faced with the increasingly severe recycling situation of waste mobile phones, many experts and scholars have demonstrated and analyzed the construction of a recycling model for waste mobile phones (Xi and Zhang, 2018; Gu et al., 2019; Li et al., 2019; Chen and Jia, 2020), including the determination of recycling prices (Peng, 2018; Yao et al., 2018), and the assumption of recycling responsibility (Xu et al., 2016; Jin et al., 2018; Zhang et al., 2019; Liu and Xu, 2020; Wang et al., 2021b). With the deepening of such research, the factors that affect waste mobile phone recycling have also attracted more attention. The socio-economic characteristics of consumers, such as gender, age, education level, family income and other objective characteristics, or their subjective intentions like environmental attitudes, social norms, environmental policies, and mobile phone use habits, have been found to affect their attitudes towards recycling waste mobile phones (Echegaray and Hansstein, 2017; Lu et al., 2019). In addition, external factors such as the improvement of mobile phone recycling policies and systems, the process, price and reputation of the mobile phone recycling platform also have an impact on the mobile phone recycling behavior of consumers (Li and Feng, 2019; Xie and Cai, 2022). Compared with developed countries, there are certain constraints in China such as an imperfect recycling policy system for waste mobile phones, spontaneity, gratuitous and insufficient recycling channels of the formal recycling platforms, and illegally paid recycling competition (Yin et al., 2014). Therefore, domestic academics have focused more on the platform side, and are committed to improving platform construction (Wu et al., 2019), pricing evaluation (Han et al., 2019), the deposit system (Li, 2021) and other aspects. Relatively speaking, there is less research on consumers’ comprehensive demand for waste mobile phone recycling, especially a lack of quantitative research on the preferences of college students for waste mobile phone recycling and willingness-to-pay.
Based on previous research results, this study conducted a more comprehensive investigation of the factors influencing waste mobile phone recycling from the perspectives of both consumers and mobile phone recycling platforms. At the same time, the choice experiment method and the mixed logit model were used to better show the real willingness and internal needs of consumers, providing a basis for determining a reasonable amount of payment.

3 Methods

3.1 Discrete choice experiment method

The Discrete Choice Experiment Method (DCE), derived from new consumption theory (Lancaster, 1966) and random utility theory (Thurstone, 1927), is the current frontier method for evaluating the non-market value of the ecological environment and natural resources. Its application to ecological environmental protection (Mao et al., 2017; Wang and Li, 2020; Yan et al., 2021), garbage recycling and management (Jin and Wang, 2005; Jin and Wang, 2006; Jia and Zhao, 2021a; Jia and Zhao, 2021b) and other fields have also gradually improved and matured in China. The discrete choice experiment method assumes that there must be a monetary value attribute in the set of all possible attributes of the research problem, that is, there is a WTP to change the current situation. Through questionnaires, an intuitive and real simulated experimental situation is created, and the respondents are provided with choice sets composed of different attribute levels of environmental items, so that they can choose their favored option from each choice set based on their own utility. Through this process, a large amount of information about individual preferences can be obtained, and the econometric model can be used to analyze the marginal values of the different attribute levels, and measure the values of the combinations of different attribute levels (Zhao et al., 2017).

3.2 Attribute and level selection

Choosing the appropriate attributes and various levels are the premises of using discrete choice experiments for accurate pricing. On the one hand, the selection of attributes should be consistent with the purpose and plan of the research, and on the other hand, the practical significance and the cognitive level of the respondents should be considered (Tan, 2016). According to the requirements of the discrete choice experiment, the interviewed college students should have a certain understanding and cognition of the attributes involved in the questionnaire. Based on the specific situation of waste mobile phone recycling by college students, we grouped the attributes into five aspects.
First, the current mobile phone recycling was mainly divided into two modes: traditional offline recycling and online recycling. Since most college students are proficient in using the Internet, the appropriate usage of Internet factors may be beneficial for significantly improving the recycling rate (Gao et al., 2013; Liu, 2015). Therefore, recycling mode is one of the attributes that affect the recycling of waste mobile phones.
Secondly, the recycling price has been affirmed and recognized by many scholars as an important factor affecting mobile phone recycling (Song et al., 2016; Dias et al., 2018; Popescu et al., 2018). It is generally believed that the higher the recycling price, the greater the enthusiasm of consumers for mobile phone recycling.
Thirdly, as a necessary personal item for contemporary life, mobile phones store a lot of private information. However, Chinese technology for deleting the personal private information in mobile phones is still immature, and there is a lack of relevant laws and regulations for protecting personal privacy in the recycling of waste mobile phones. Some college students may avoid recycling over concerns about the leakage of private information (Jiang, 2019). Therefore, information security is also an important consideration that affects recycling behavior (Lu, 2020).
The payment amount refers to the amount that consumers are willing to pay or mortgage for the recycling of waste mobile phones, which is an essential recycling attribute. By studying the previous literature and a certain scale of pre-investigation (Zhong et al., 2014), the range of deposits acceptable to consumers is known to be concentrated in the range of 11% to 15%, so we determined the payment amount level of college student consumers for recycling each waste mobile phone as 0 yuan, 5 yuan, 10 yuan, and 20 yuan.
Finally, for the payment amount, some platforms choose to charge separately during recycling, and some directly increase the cost of future recycling in the current price of the mobile phone in order to simplify the charging process. This difference in payment methods may also affect consumers’ WTP for recycling.
The specific attributes and level settings of waste mobile phone recycling are shown in Table 1. In order to avoid differences in public policy awareness and feedback caused by the heterogeneity of college students, the personal characteristic variables and environmental protection cognition variables of 12 interviewed college students were included.
Table 1 Attributes and level settings of WTP for waste mobile phone recycling
Attributes Attribute description Level settings
Recycling mode The recycling method selected when recycling used mobile phones: offline recycling such as second-hand markets, community recycling, reclamation depot, or online recycling on websites. Traditional recycling mode; Internet + recycling
Recycling price Recycler’s quotation for waste mobile phones High; Low
Information safety Cleaning of personal privacy in mobile phones Non-guaranteed; Guaranteed
Payment amount The amount that college consumers are willing to pay or pledge in the recycling process of used mobile phones 0 yuan; 5 yuan; 10 yuan; 20 yuan
Payment method The method chosen by college students during the recycling and disposal of waste mobile phones Pay separately when recycling; Included in the price when purchasing mobile phones

3.3 Orthogonal experimental design

Five experimental index attributes were incorporated into this study. According to the total factor design, 64 (2×2×2×4×2) alternatives can be formed, which are difficult to realize completely in the questionnaire. Therefore, an orthogonal experimental design was carried out to design the questionnaire, and four groups of 12 choice sets were obtained, in which each choice set contained three schemes. The respondent could choose scheme 1, scheme 2, or to stay with the status quo (see Table 2). The “choose neither of the two schemes” option provided respondents with a more realistic market scenario and also prevented respondents from having to choose an option they did not prefer, which would have led to an overestimation of consumer willingness- to-pay.
Table 2 Choice set 1
Attribute Recycling mode Recycling price Information safety Payment amount Payment method
Scheme 1 Traditional recycling mode High Non-guaranteed 0 yuan Pay separately when recycling
Scheme 2 Internet + recycling Low Guaranteed 10 yuan Included in the price when purchasing mobile phones
Scheme 3 I would like to choose neither of the two schemes

3.4 Attribute variable settings

The definitions and explanations of each attribute variable for the recycling of waste mobile phones are shown in Table 3. Traditional recycling mode, low recycling price, information insecurity and additional recycling fees were taken as the reference variables to explore the utility of the five attribute variables.
Table 3 Definitions and interpretations of the variables
Variable attribute Name Assignment Interpretation
Explained variable Scheme selection result Unchecked=0, checked=1 Categorical variable
Attribute explanatory variable Payment amount 0, 5, 10, 20 yuan Continuous variable, expected reduced utility
Recycling mode traditional recycling mode =0 Categorical variables, Reference variable
Internet + recycling mode =1 Categorical variables, expected to increase utility
Recycling price Low recycling price =0 Classification variable
High recycling price =1 Categorical variables, expected to increase utility
Information security Information insecurity =0 Classification variable
Information security =1 Categorical variables, expected to increase utility
Payment method Pay separately when recycling =0 Classification variable
Included in the price when purchasing mobile phones =1 Categorical variables, expected to increase utility

3.5 Model specification

The Multi-nomial Logit (MNL) model is the simplest and most widely used method for analyzing the data of a discrete choice experimental questionnaire, but it is based on the IIA (Independence of Irrelevant Alternatives) assumption. If this assumption is violated by the design of the choice set, then the estimation results of this model would be biased, and the sequence of respondents’ preferences would also be affected. Compared with the MNL model, the Mixed Logit model, which can avoid or reduce the deviations caused by violating the IIA assumption, is more flexible and credible, and has better fitting and prediction accuracy (Johnston and Duke, 2007; Huh et al., 2015). Therefore, we used the Mixed Logit model to evaluate the WTP of respondents, and retained the regression results of the MNL model that only included the attribute characteristics for comparative analysis.
The utility that college student consumer n obtains from recycling waste mobile phones can be expressed by U. Therefore, the utility of consumer n can be expressed as:

Unit=Vnit+εnit

where Unit means the utility function of consumer n while choosing the choice set i under the situation t, which is composed of the observable part Vnit and the random disturbance term εnit .
When Unit>Unjt, then compared to the choice set j, the choice set i can bring greater utility to the consumer, and consumer n will choose choice set i.

Pnit=Prob(Unit>Unjt)=Prob(Vnit+εnit>Vnjt+εnjt)

=Prob(Vnit-Vnjt>εnit-εnjt); i≠j

where Pnit is the probability that consumer n chooses the recycling scheme represented by choice set i; Unit and Unjt mean the utility function of consumer n while choosing the choice set i and set j under the situation t, which are composed of the observable part Vnit, Vnjt, respectively, and the random disturbance term εnit.
The distribution of the random disturbance term determines the calculation result of the probability. The Mixed Logit model usually assumes that β is a random variable that obeys a certain probability distribution, so it overcomes the problem that the MNL model requires the IIA assumption, and can reflect the randomness of personal preferences. The heterogeneity of preferences of college students can also be analyzed. The probability that consumer n chooses choice set i can be represented by a Mixed Logit model:
P n i t   = ∫ ​ exp V n i t exp ∑ ​ V n j t f ( β ) d β
where f(β) is the density function of a certain distribution of β; Vnit and Vnjt mean the observable utility function of consumer n while choosing the choice set i and set j under the situation t, respectively.
Assuming that the consumer utility obtained from each attribute level of waste mobile phone recycling is additive, V in equation (2) can be expressed as:
V = ∑ ​ β k X k
where Xk is the k-th attribute of the waste mobile phone recycling scheme, and βk is the marginal utility of the k-th recycling attribute.
M R S 12 = ∂ V / ∂ X 1 ∂ V / ∂ X 2 = β 1 / β 2
where MRS12 means the marginal rates of substitution for recycling attributes X1 and X2, V is the observable utility function, and β1 and β2 are the marginal utilities of the first and second recycling attributes.

WTP=βk/(-βcost)

where βk is the marginal utility of the k-th recycling attribute, so βcost is assumed as the marginal utility of the cost paid for a recycling attribute of waste mobile phones, and the WTP is the marginal willingness to pay for the k-th recycling attribute. That is, when the levels of other recycling attributes remain unchanged, it is the highest premium that one is willing to pay to obtain the recycling attribute Xk.

4 Materials

4.1 Sample selection

Beijing, as a national cultural center, gathers numerous colleges and universities, and has many students from different backgrounds. The willingness and participation of college students in Beijing in recycling waste mobile phones are both typical and universal. In recent years, with the acceleration of mobile phone updates and iterations, the number of waste mobile phones retained by college students is increasing. Therefore, understanding their WTP and demand preferences for recycling waste mobile phones, promoting their participation in recycling waste mobile phones, and establishing a sound recycling system for waste mobile phones have become the important means for increasing the recycling rate of waste mobile phones.
Based on the these considerations, we conducted a questionnaire survey on the WTP for waste mobile phone recycling among college students in Haidian, Fangshan and Changping Districts, where the universities in Beijing are aggregated. The survey was conducted in the form of face-to-face interviews, and was mainly composed of three parts. First, the respondents’ cognition and behavior were assessed, including their knowledge about the ecological benefits of waste mobile phone recycling, the recycling policies and laws, discounted value, information security, and participation willingness and frequency in recycling. The second part was the choice sets which indicated the respondents’ awareness of the importance of attributes and preference for the attribute levels. The third part was the respondents’ personal socio-economic characteristics, including age, gender, education, monthly consumption and others. The questionnaire followed the trend of the respondents' psychological changes, and adopted an objective description and step-by-step guidance to explain to the respondents the main responsibility, recycling channels, information protection and other issues of waste mobile phone recycling. Respondents were also informed that paying for the recycling of waste mobile phones would bring more benefits, such as the improvement of the recycling system and the protection of their own information security, which made it easier for the respondents to understand the survey and improved the survey efficiency.
The Scheaffer sampling formula was used to test the sample capacity:
n = N N − 1 × δ 2 + 1
where n represents the sample size, N represents the total number of college students in the sampling area, and δ represents the sampling error. According to the Beijing Statistical Yearbook 2020, the total number of college students in Beijing (undergraduate plus graduate students) was about 800000. Therefore, given N=800000 and δ=0.05, it was estimated that at least 400 questionnaires were needed. Combined with the development of higher education, the consumption of mobile phones, and the economic situation of each district in Beijing, a combination of typical sampling and random sampling was used to select the universities in three places, Xueyuan Road in Haidian District, Liangxiang University Town in Fangshan District, and Shahe Higher Education Park in Changping District, as the main research sites. A total of 783 questionnaires were distributed. After eliminating 25 questionnaires with obvious logical errors, 758 valid questionnaires were finally obtained, so the effective recovery rate was 96.8%.

4.2 Personal characteristics and cognitive variables of respondents

The personal characteristic variables and cognitive behavioral variables of the respondents are shown in Table 4. Among the college students interviewed, 424 were male and 334 were female, so there was an appropriate ratio of males to females. The educational level of 684 undergraduates, 64 masters, and 10 doctors met the basic educational characteristics of the survey respondents. Nearly 60% of the respondents had an annual household income of less than 100000 yuan, and more than 70% of the respondents had an average monthly consumption of less than 2000 yuan, which was relatively representative. Among the respondents, 87.42% of them owned more than one mobile phone, 52.64% of them owned 2-3 mobile phones, 22.01% of them owned 4-5 mobile phones, and 11.95% of them owned more than five mobile phones. Based on the number of mobile phones held, nearly 90% of the respondents clearly had waste mobile phones, but nearly 50% of the respondents had no idea about the discounted value of waste mobile phones. Only 0.63% of the respondents were very familiar with the discounted value of mobile phones.
Table 4 Descriptive statistics of the respondents’ personal characteristics and cognitive variables
Variable Assignment Number of respondent Proportion (%) Average
Gender Male=1 424 55.94 0.56
Female=0 334 44.06
Age Under 18=1 24 3.14 1.99
18-25 years old=2 715 94.34
26-30 years old=3 19 2.52
Over 30=4 0 0
Education Undergraduate=1 684 90.24 1.11
Master=2 64 8.44
Doctoral students=3 10 1.32
Annual household income (yuan) Below 50000=1 119 15.72 2.43
[50000, 100000)=2 310 40.88
[100000, 200000)=3 210 27.67
over 200000=4 119 15.72
Personal monthly average consumption (yuan) Below 1000=1 91 11.95 2.16
[1000, 2000)=2 496 65.41
[2000, 3000)=3 133 17.61
Over 3000 =4 38 5.03
Number of mobile phones owned by each individual 1=1 95 12.58 2.34
[2, 3]=2 407 53.64
[4, 5]=3 167 22.01
Over 5=4 91 11.95
Understanding of the discounted price of mobile phones Don’t know=1 372 49.06 1.58
Know a little=2 338 44.65
Better understanding=3 43 5.66
Very familiar=4 5 0.63
Views on environmental pollution and resource waste caused by waste mobile phones No effect=1 19 2.52 3.34
Not serious=2 95 12.58
General=3 324 42.77
Severe=4 248 32.70
Very serious=5 71 9.43
Doubts about the leakage of personal information in the recycling of used mobile phones Must exist=1 248 32.70 1.98
Presence=2 277 36.48
Possible=3 233 30.82
Not present=4 0 0
Definitely not present=5 0 0
Understanding of policies or laws related to the recycling of used mobile phones Don’t know=1 615 81.13 1.24
Know something=2 105 13.84
General=3 38 5.03
Better understanding=4 0 0
Very familiar=5 0 0
Supportive attitude towards mobile phone recycling Not supported at all=1 5 0.63 3.63
Unsupported=2 29 3.77
Comparison support=3 353 46.54
Support=4 229 30.19
Very supportive=5 143 18.87
Times of participating in the recycling of waste mobile phones None=1 167 22.02 1.98
Once=2 434 57.23
2 or more times=3 157 20.75
Besides the influences of personal characteristics, respondents’ WTP may also be related to their perceptions of mobile phone recycling. According to the statistics, nearly 85% of the respondents believed that if waste mobile phones were not effectively recycled, serious environmental pollution and wasting of resources would result. At the same time, 69.18% of the respondents firmly believed that there was a risk of personal information leakage when recycling waste mobile phones, and the remaining of the respondents were worried that the information leakage problem would exist. More than 80% of the respondents said that they did not understand the laws and policies related to the recycling of waste mobile phones at all, indicating that the current awareness level was low, so it was difficult for consumers to protect their legitimate rights and interests in accordance with the law when they encountered problems with mobile phone recycling. A total of 77.98% of the respondents had participated in the recycling of waste mobile phones, of which 20.75% had participated two or more times, and more than 95% of the respondents expressed their willingness to recycle waste mobile phones. These high percentages indicated that most of the current college students had participated in recycling waste mobile phones, and also had a general knowledge of the recycling process and a strong willingness to support it.

5 Results

5.1 Mixed Logit Model analysis

The recovered valid questionnaires were analyzed using Stata16.0 software, and an econometric model was used to estimate the marginal rate of substitution for each attribute, that is, the marginal utility of each attribute to the college students. In order to explore the influences of the personal characteristics of the respondents on the utility level of mobile phone recycling, two models were established. The MNL model (Model 1) assumed that the respondents’ choices were only related to the attribute level of the recycling of waste mobile phones, while the Mixed Logit model (Model 2) took the individual socio-economic characteristics of the respondents into consideration.
The model regression results are shown in Table 5. In Model 1, the five variables of payment amount, recovery mode, recovery price, information security, and payment method, all had significant impacts on the utility-scale of the respondents (P<0.01). As expected, an increase in the payment amount for waste mobile phone recycling will reduce the utility-scale of the college students; the Internet + recycling mode will increase the utility-scale; higher recycling prices will increase the utility-scale; the security guarantee of personal privacy will increase the utility-scale; and including the recycling cost in the price of mobile phones will increase the utility-scale. The results of Model 2 show that adding the factors of individual socio-economic characteristics of college students will not affect the significance or direction of the coefficients.
Table 5 Model estimation results on waste mobile phone recycling scheme decisions
Variables Model 1 Model 2
Coeffients Z-value Coeffients Z-value
Payment amount -0.058*** -3.03 -0.069*** -3.43
Recycling mode 0.190*** 3.71 0.198*** 3.79
Recycling price 0.416*** 5.92 0.410*** 5.6
Information security 1.919*** 9.81 2.073*** 9.85
Payment method 0.272*** 4.69 0.304*** 5.03
Gender - - 1.234*** 4.84
Age - - 0.207 1.48
Education - - -0.298 -1.47
Annual household income - - -0.294*** -4.34
Personal monthly average consumption - - -0.045 -0.46
Number of mobile phones owned by the individuals - - -0.222*** -3.12
Understanding of the discounted price of mobile phones - - -0.415*** -4.64
Views on environmental pollution and resource waste caused by waste mobile phones - - 0.399*** 6.17
Doubts about the leakage of personal information in the recycling of used mobile phones - - 0.228*** 3.23
Understanding of policies or laws related to the recycling of used mobile phones - - 0.175 1.46
Supportive attitude towards mobile phone recycling - - 0.646*** 8.76
Times of participating in the recycling of waste mobile phones - - 0.583*** 6.51
Log likelihood -2672.1332 -2416.5242
Pseudo R2 0.0308 0.1235

Note: *** means P<0.01.

After adding the personal socio-economic characteristics, the utility-scale of the five recycling attributes changed slightly, and the model fitting was optimized. Among the newly added personal characteristic variables, age, education, and personal monthly consumption had no significant impact on the choice set of waste mobile phone recycling, which may be due to the fact that the research objects of this study were college students, and the degree of discrimination on these three types of features was small. Therefore, no significant difference was evident in the selection of recycling schemes. In addition, the mobile phone recycling policy or legal awareness had no significant impact on the choice of waste mobile phone recycling options, which was inconsistent with our expectations. Observing the data, this may be due to more than 80% of the respondents saying that they did not understand the relevant policies and laws, and therefore the policies and laws had no significant impact on the choice of recycling options.
Other than the above four variables, the remaining variables significantly affected the choice of mobile phone recycling schemes at the 1% level. Among them, gender had a significant positive impact on WTP, that is, women were more willing to pay for waste mobile phone recycling. The annual household income and the number of mobile phones held had a negative impact on the WTP for waste mobile phone recycling, which means respondents with higher household income and more mobile phones had lower WTP to participate in the recycling of waste mobile phones. The higher the household income level, the greater the elasticity of personal consumption of mobile phones, resulting in a higher number of mobile phones held. Thus, the relatively higher recycling fees that need to be paid would have a negative impact on recycling participation. At the same time, when the respondents come from a richer family, the less concerned they would be about the recycling profits of used mobile phones, and the more concerned about the harm that mobile phone recycling may cause, such as information leakage. The degree of understanding of the discounted price of waste mobile phones, and the worry about the leakage of personal information in mobile phone recycling were inversely related to the WTP. The more people know about the discounted price of waste mobile phone recycling and the more people think that information leakage in mobile phone recycling is inevitable, the lower their WTP for participating in the recycling of waste mobile phones. These correlations may be because the recycling price of waste mobile phones has generally dropped significantly compared with the purchase price, and the high “sunk cost” and concerns about information security have reduced the WTP of the respondents. Views on the environmental pollution and resource waste caused by waste mobile phones, supporting attitudes and participation in recycling each had a positive impact on the WTP for waste mobile phone recycling.

5.2 WTP analysis

The above analysis indicates that the significance and direction of the coefficients of the two models are consistent, and after adding personal socio-economic characteristics and cognitive behavior, the Pseudo R2 value of Model 2 was higher and the fitting effect was better. Therefore, we adopted Model 2 to calculate the WTP for each attribute of waste mobile phones. The attribute level coefficient is an observation used to measure the utility of the attribute. Taking the coefficient of payment amount (-0.069) in Model 2 as an example, this means that with other conditions unchanged, each increase in the payment amount will reduce the college students’ utility by 0.069 units. Taking the payment amount coefficient as the marginal utility of the cost paid for the recycling attributes of waste mobile phones, the marginal WTP for other attributes can then be calculated. The marginal value of each attribute of waste mobile phone recycling is shown in Table 6.
Table 6 College students’ WTP for the recycling of waste mobile phones
Attribute Coefficient WTP Sequence
Information security 2.073 30.04 1
Recycling price 0.410 5.94 2
Payment method 0.304 4.41 3
Recycling mode 0.198 2.87 4
Payment amount -0.069 - -
The results show that the recycling mode, payment method, recycling price and information security in the recycling of waste mobile phones influenced the scale of utility to the respondents in increasing order. Among them, when reducing or eliminating the information security risks in the recycling of waste mobile phones, the utility-scale of the interviewed college students would be improved, and they were willing to pay 30.04 yuan for each mobile phone recycled. When the recycling mode of waste mobile phones was changed from the offline ordinary recycling mode to the “Internet Plus Recycling” mode, the average interviewed college student was willing to pay 2.87 yuan for each mobile phone recycled. When the recycling price of waste mobile phone recycling had a higher room for improvement, the average interviewed college student was willing to pay 5.94 yuan for each mobile phone recycled. When the payment method for recycling waste mobile phones was changed from paying separately to being included in the price when purchasing mobile phones, the average interviewed college student was willing to pay 4.41 yuan for each mobile phone recycled.

6 Conclusions and suggestions

Based on the five attributes of recycling mode, recycling price, information security, payment amount, payment method, and other personal socio-economic characteristics, this study explored the demand preferences and WTP of college students in Beijing for the recycling of waste mobile phones by conducting a choice experiment. The mixed Logit model was constructed using Stata15.1 to quantitatively analyze the marginal WTP of college students for various attributes of waste mobile phone recycling, and some valuable research conclusions can be drawn.
(1) College students have a strong willingness to improve the information security, recycling price, recycling method and payment method of waste mobile phone recycling, but with an increase in the cost of recycling waste mobile phones, college students’ WTP decreases.
(2) The marginal WTP of college students for various attributes of waste mobile phone recycling varies greatly, ranging from 2.87 yuan to 30.04 yuan each time. The marginal WTP from high to low is information security, recycling price, payment method and recycling mode.
(3) Personal socio-economic attributes such as gender, annual household income, and the number of mobile phones held will have a significant impact on the WTP for mobile phone recycling. Awareness of the pollution caused by waste mobile phones, an understanding of the waste mobile phone recycling policies, and the willingness and behavior to participate in recycling will have a significant positive impact on college students’ preferences for waste mobile phone recycling.
(4) Although the questionnaire shows that the current college students in Beijing have a strong willingness to support mobile phone recycling, most of the respondents do not have a deep understanding of either the pollution status of waste mobile phones or the mobile phone recycling processes and related policies, and they are concerned about the possible information leakage related to mobile phone recycling. At the same time, China’s mobile phone recycling industry system needs to be improved. There are deficiencies in legislation and regulations on waste mobile phone recycling, standardizing mobile phone recycling platforms, electronic waste treatment technology, and implementing the extended producer responsibility system. From the perspective of consumer willingness to pay, this study examined both the subjective and objective factors that may have an impact. The results demonstrated that the two-pronged approach of building a reasonable waste mobile phone recycling management system while improving consumers’ environmental awareness and recycling enthusiasm is of great significance for solving the current situation of the low recycling rate of waste mobile phones.
Based on these conclusions, we put forward the following four suggestions.
(1) Increase the publicity of waste mobile phone recycling. The relevant government departments should strengthen the publicity activities related to waste mobile phones, so that more people will understand the construction and development of the waste mobile phone recycling system, and understand the positive role of waste mobile phone recycling in their personal life, daily production and the natural ecology. Through mobile phone replacement and other ways more people can become reacquainted with and truly participate in the construction, development and improvement of the waste mobile phone recycling system, in order to improve the recycling rate of waste mobile phones from the source.
(2) Improve information security protection capabilities. Government departments and recycling enterprises should jointly build a systematic, standardized and diversified recycling system for waste mobile phones, and formulate relevant laws and policies to supervise and manage the entire recycling system. Information security is the issue of greatest concern for consumers. On the one hand, manufacturers should realize the application and promotion of mobile phone information removal technology, and deal with the privacy of mobile phone information. On the other hand, the government should increase its crackdown on the leakage of mobile phone personal information, and can also entrust a third-party platform to monitor the whole process of the recycling of waste mobile phones.
(3) Establish a fair and transparent price system. Currently, many methods are applied to the valuation of waste mobile phones, including the value appraisal method based on market comparisons, the value appraisal method based on a fuzzy comprehensive evaluation mathematical model, and the valuation method based on a fuzzy neural network. Using these methods for comprehensive comparison, it is possible to formulate an effective and reasonable price for recycling waste mobile phones, and publicly releasing the price on the network platform will allow consumers, especially college students, to have a more intuitive and systematic understanding of the price.
(4) Pay attention to consumer convenience service needs. The recycling of waste mobile phones should pay attention to consumers, especially college students, regarding the convenience of recycling and payment methods, in order to realize the development of the recycling mechanism through the radiation of college students. Whether it involves a waste mobile phone recycling platform or a government department, it is necessary to take consumer experience improvement and personalized utility as the premise, and to reduce the consumption of consumers’ time and energy in the process of recycling waste mobile phones.

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